{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14e9cfe1",
   "metadata": {},
   "source": [
    "# Demo - pyTrigger\n",
    "### Usage example\n",
    "\n",
    "The purpose of this demo is to show the main functionalities and usages of the `trigger` python package.\n",
    "\n",
    "The entire workflow of processing, indeed, is based on the correct usage of the object exposed by the package and called `TriggerDB`.\n",
    "\n",
    "First of all, lets start by including the module in our python script as follow:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "262b31dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trigger import TriggerDB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd61f8f",
   "metadata": {},
   "source": [
    "Now we can create an instance of the object by using a sequential code or a context (*recommended*).\n",
    "\n",
    "The second option is recommended since it guarantees the correct *logout* of the account at the end of the execution.\n",
    "\n",
    "| 🚩 Note                        |\n",
    "|:-------------------------------|\n",
    "| For the correct configuration of the database account, at the first usage of the object the package will ask us to insert a *email* and *password* credentials; these information will be saved into a secure location and they will be automatically used in any future setup |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c9cba90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;40m[INFO]\u001b[0m Credentials loaded successfully\n",
      "<trigger.db.TriggerDB object at 0x78b9280fa2a0>\n",
      "\u001b[38;5;40m[INFO]\u001b[0m Logout success\n"
     ]
    }
   ],
   "source": [
    "with TriggerDB() as db:\n",
    "  print(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae68f686",
   "metadata": {},
   "source": [
    "As you can notice, an automatic logging of the status of the server is produced, highlighting the correct setup of the account and the final logout.\n",
    "\n",
    "The `db` object has a series of private and public member functions: we discourage the usage of the private function unless particular needs.\n",
    "\n",
    "The entire list of member functions could be listed below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3baf793b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;40m[INFO]\u001b[0m Credentials loaded successfully\n",
      "['accounts', 'columns', 'from_', 'num_elements', 'select', 'tables']\n",
      "\u001b[38;5;40m[INFO]\u001b[0m Logout success\n"
     ]
    }
   ],
   "source": [
    "with TriggerDB() as db:\n",
    "  print([x for x in dir(db) if not x.startswith('_')])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd27ba10",
   "metadata": {},
   "source": [
    "Each member function could be used to obtain a different set of information.\n",
    "\n",
    "The main functionalities for the management of the database tables are described by the following functions:\n",
    "\n",
    "* *tables* ➡️ Get the list of available tables\n",
    "* *columns* ➡️ Get the list of available columns in the current table\n",
    "* *accounts* ➡️ Get the list of available accounts\n",
    "\n",
    "A common usage of the database, indeed, involves the extraction of records belonging to a particular user account, so, first of all, we need to know the available tables, the set of information stored in that table and the list of possible accounts to check.\n",
    "Lets start by the list of available accounts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6280a3a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;40m[INFO]\u001b[0m Credentials loaded successfully\n",
      "\u001b[38;5;208m[WARN]\u001b[0m Maximum limit exceeded. Given 100000 against a maximum of 10000. Automated downgrade to the maximum value\n",
      "[{'email': 'n.curti1'}, {'email': 'a.merlot'}, {'email': 'cnr00001'}, {'email': 'DE000003'}, {'email': 'CH000004'}, {'email': 'CH000005'}, {'email': 'GR000006'}, {'email': 'GR000007'}, {'email': 'GR000008'}, {'email': 'CH000009'}, {'email': 'DE000010'}, {'email': 'DE000011'}, {'email': 'GR000012'}, {'email': 'DE000013'}, {'email': 'DE000014'}, {'email': 'DE000015'}, {'email': 'GR000016'}, {'email': 'DE000017'}, {'email': 'GR000018'}, {'email': 'DE000019'}, {'email': 'DE000020'}, {'email': 'GR000021'}, {'email': 'DE000022'}, {'email': 'DE000023'}, {'email': 'DE000024'}, {'email': 'GR000025'}, {'email': 'GR000026'}, {'email': 'GR000027'}, {'email': 'GR000028'}, {'email': 'GR000029'}, {'email': 'GR000030'}, {'email': 'GR000031'}, {'email': 'GR000032'}, {'email': 'GR000033'}, {'email': 'GR000034'}, {'email': 'GR000035'}, {'email': 'GR000036'}, {'email': 'GR000037'}, {'email': 'GR000038'}, {'email': 'GR000039'}, {'email': 'GR000040'}, {'email': 'GR000041'}, {'email': 'GR000042'}, {'email': 'GR000043'}, {'email': 'GR000044'}, {'email': 'GR000045'}, {'email': 'GR000046'}, {'email': 'GR000047'}, {'email': 'GR000048'}, {'email': 'GR000049'}, {'email': 'GR000050'}, {'email': 'GR000051'}, {'email': 'GR000052'}, {'email': 'GR000053'}, {'email': 'GR000054'}, {'email': 'GR000055'}, {'email': 'GR000056'}, {'email': 'DE000057'}, {'email': 'IT000058'}, {'email': 'IT000059'}, {'email': 'IT000060'}, {'email': 'IT000061'}, {'email': 'IT000062'}, {'email': 'IT000063'}, {'email': 'IT000064'}, {'email': 'IT000065'}, {'email': 'IT000066'}, {'email': 'GR000067'}, {'email': 'GR000068'}, {'email': 'IT000069'}, {'email': 'GR000070'}, {'email': 'GR000071'}, {'email': 'GR000072'}, {'email': 'GR000073'}, {'email': 'GR000074'}, {'email': 'GR000075'}, {'email': 'DE000076'}, {'email': 'DE000077'}, {'email': 'DE000078'}, {'email': 'DE000079'}, {'email': 'DE000080'}, {'email': 'DE000081'}, {'email': 'DE000082'}, {'email': 'DE000083'}, {'email': 'DE000084'}, {'email': 'DE000085'}, {'email': 'DE000086'}, {'email': 'DE000087'}, {'email': 'DE000088'}, {'email': 'DE000089'}, {'email': 'DE000090'}, {'email': 'DE000091'}, {'email': 'IT000092'}, {'email': 'GR000093'}, {'email': 'GR000094'}, {'email': 'GR000095'}, {'email': 'GR000096'}, {'email': 'GR000097'}, {'email': 'GR000098'}, {'email': 'GR000099'}, {'email': 'GR000100'}, {'email': 'm.carell'}, {'email': 'i.diembe'}, {'email': 's.disaba'}, {'email': 'e.bratti'}, {'email': 'a.hoffma'}, {'email': 'c.perdik'}, {'email': 'a.spadot'}, {'email': 'CH000108'}, {'email': 'IT000109'}, {'email': 'IT000110'}, {'email': 'GR000111'}, {'email': 'GR000112'}, {'email': 'CH000113'}, {'email': 'CH000114'}, {'email': 'CH000115'}, {'email': 'CH000116'}, {'email': 'CH000117'}, {'email': 'CH000118'}, {'email': 'DE000119'}, {'email': 'DE000120'}, {'email': 'DE000121'}, {'email': 'DE000122'}, {'email': 'DE000123'}, {'email': 'DE000124'}, {'email': 'DE000125'}, {'email': 'CH000126'}, {'email': 'CH000127'}, {'email': 'CH000128'}, {'email': 'GR000129'}, {'email': 'CH000130'}, {'email': 'IT000131'}, {'email': 'CH000132'}, {'email': 'IT000133'}, {'email': 'GR000134'}, {'email': 'GR000135'}, {'email': 'GR000136'}, {'email': 'GR000137'}, {'email': 'IT000138'}, {'email': 'IT000139'}, {'email': 'GR000140'}, {'email': 'a.fuschi'}, {'email': 'GR000142'}, {'email': 'IT000143'}, {'email': 'GR000144'}]\n",
      "\u001b[38;5;40m[INFO]\u001b[0m Logout success\n"
     ]
    }
   ],
   "source": [
    "with TriggerDB() as db:\n",
    "  print(db.accounts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bed06eb",
   "metadata": {},
   "source": [
    "This is out first query on the database and you can notice that the output is a standard JSON made by a list of dictionaries.\n",
    "\n",
    "To facilitate the readability of the output we can use the `pandas` package to convert it into a dataframe as follow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d95ae74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;40m[INFO]\u001b[0m Credentials loaded successfully\n",
      "\u001b[38;5;208m[WARN]\u001b[0m Maximum limit exceeded. Given 100000 against a maximum of 10000. Automated downgrade to the maximum value\n",
      "\u001b[38;5;40m[INFO]\u001b[0m Logout success\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>n.curti1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a.merlot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cnr00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DE000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CH000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>GR000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>a.fuschi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>GR000142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>IT000143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>GR000144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        email\n",
       "0    n.curti1\n",
       "1    a.merlot\n",
       "2    cnr00001\n",
       "3    DE000003\n",
       "4    CH000004\n",
       "..        ...\n",
       "140  GR000140\n",
       "141  a.fuschi\n",
       "142  GR000142\n",
       "143  IT000143\n",
       "144  GR000144\n",
       "\n",
       "[145 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "with TriggerDB() as db:\n",
    "  df = pd.json_normalize(db.accounts())\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829a1db5",
   "metadata": {},
   "source": [
    "In this way we can easily manipulate the amount of information get by the query and see the amount of accounts registered in the current database.\n",
    "\n",
    "The next processing involves the available tables and information stored in them.\n",
    "\n",
    "To this purpose we can use the combination of *tables* and *columns* member functions as follow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8847bc2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;40m[INFO]\u001b[0m Credentials loaded successfully\n",
      "Available tables: ['myair', 'ecg', 'ppg', 'gps', 'sleep', 'smartwatchlow', 'smartwatchhigh', 'accounts']\n",
      "  myair: ['email', 'userId', 'year', 'month', 'day', 'hour', 'minute', 'second', 'pm1', 'pm25', 'pm10', 'pc03', 'pc05', 'pc1', 'pc25', 'pc5', 'pc10', 'temperature', 'humidity', 'pressure', 'sound', 'uvb', 'light']\n",
      "  ecg: ['email', 'userId', 'year', 'month', 'day', 'hour', 'minute', 'second', 'microsecond', 'ecg']\n",
      "  ppg: ['email', 'userId', 'year', 'month', 'day', 'hour', 'minute', 'second', 'microsecond', 'ppg']\n",
      "  gps: ['email', 'userId', 'year', 'month', 'day', 'hour', 'minute', 'second', 'longitude', 'latitude', 'accuracy']\n",
      "  sleep: ['email', 'userId', 'year', 'month', 'day', 'hour', 'minute', 'second', 'sleepduration', 'awake', 'insomnia', 'remsleep', 'lightsleep', 'deepsleep', 'sleepquality']\n",
      "  smartwatchlow: ['email', 'userId', 'year', 'month', 'day', 'hour', 'minute', 'second', 'step', 'cal', 'bphigh', 'bplow', 'bodytemp']\n",
      "  smartwatchhigh: ['email', 'userId', 'year', 'month', 'day', 'hour', 'minute', 'second', 'heartrate', 'sleeprate', 'oxygens']\n",
      "  accounts: ['id', 'email', 'created_at', 'last_login']\n",
      "\u001b[38;5;40m[INFO]\u001b[0m Logout success\n"
     ]
    }
   ],
   "source": [
    "with TriggerDB() as db:\n",
    "  tables = db.tables()\n",
    "  print(f'Available tables: {tables}')\n",
    "  for tab in tables:\n",
    "    print(f'  {tab}: {db.columns(tab)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c1bda6",
   "metadata": {},
   "source": [
    "From this overview we can notice that each column has always an *email* column which lists the information get by our previous analysis about the amount of accounts.\n",
    "The *email* information represents the **link** between the users and the information stored into the several tables and it could be used to extract the data associated to each account.\n",
    "\n",
    "| 🚩 Note                        |\n",
    "|:-------------------------------|\n",
    "| The list of variable types is described in the online documentation available [here](https://trigger-io.difa.unibo.it/apiDocs.html) |\n",
    "\n",
    "Starting from these information, we are now ready to perform our first (realistic) query on the database.\n",
    "\n",
    "To this purpose, the `trigger` package provides two different interface: *standard* and *chaining*.\n",
    "\n",
    "For a better readability of the code and a correct management of the desired query, we encourage the use of the *chaining* modality: for sake of completeness, the *standard* way can be easily addressed by calling the `select` member function of the object.\n",
    "\n",
    "The chaining modality could be used by calling the `from_` member function which requires the table that we want to analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47f2c76c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;40m[INFO]\u001b[0m Credentials loaded successfully\n",
      "\u001b[38;5;40m[INFO]\u001b[0m Logout success\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>email</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>78</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>60</td>\n",
       "      <td>1013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>80</td>\n",
       "      <td>1013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "      <td>991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>52</td>\n",
       "      <td>965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>78</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>77</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>76</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>74</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>DE000086</td>\n",
       "      <td>2025</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>72</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>345 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        email  year  month  day  hour  minute  second  temperature  humidity  \\\n",
       "0    DE000086  2025      9   10     9      56       4           27        78   \n",
       "1    DE000086  2025      9   10    10       0       4           28        60   \n",
       "2    DE000086  2025      9   10    10       4       4           28        80   \n",
       "3    DE000086  2025      9   10    10       8       4           29        54   \n",
       "4    DE000086  2025      9   10    10      12       4           28        52   \n",
       "..        ...   ...    ...  ...   ...     ...     ...          ...       ...   \n",
       "340  DE000086  2025      9   10    11      58       4           27        78   \n",
       "341  DE000086  2025      9   10    12       2       4           29        77   \n",
       "342  DE000086  2025      9   10    12       6       4           30        76   \n",
       "343  DE000086  2025      9   10    12      10       4           31        74   \n",
       "344  DE000086  2025      9   10    12      14       4           31        72   \n",
       "\n",
       "     pressure  \n",
       "0        1015  \n",
       "1        1013  \n",
       "2        1013  \n",
       "3         991  \n",
       "4         965  \n",
       "..        ...  \n",
       "340      1012  \n",
       "341      1012  \n",
       "342      1012  \n",
       "343      1012  \n",
       "344      1012  \n",
       "\n",
       "[345 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with TriggerDB() as db:\n",
    "  res = (\n",
    "    db.from_('myair')\n",
    "      .select('email', 'year', 'month', 'day', 'hour', 'minute', 'second', 'temperature', 'humidity', 'pressure')\n",
    "      .where(year='=2025', month='=9', day='=10', hour='>=0', email='=DE000086')\n",
    "      .order_by('second')\n",
    "      .asc()\n",
    "      .limit(10000)\n",
    "      .fetch()\n",
    "  )\n",
    "\n",
    "df = pd.json_normalize(res)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21be81f6",
   "metadata": {},
   "source": [
    "| 🚩 Note                        |\n",
    "|:-------------------------------|\n",
    "| The execution time of the query is drastically affected by the amount of data stored in the database **and** by the `order_by` criteria; for this reasons also a \"simple\" query could require an execution time of the order of minutes (!) |\n",
    "\n",
    "Now we can easily display the trend of the climate variables for the selected day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a7aa1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "time = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute', 'second']], format='%Y-%m-%d %H:%M:%S.%f')\n",
    "\n",
    "with sns.plotting_context('paper', font_scale=1.5):\n",
    "  fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 5))\n",
    "  sns.lineplot(\n",
    "    ax=ax,\n",
    "    x=time,\n",
    "    y=df['temperature'],\n",
    "    color='orange',\n",
    "  )\n",
    "  ax1 = ax.twinx()\n",
    "  sns.lineplot(\n",
    "    ax=ax1,\n",
    "    x=time,\n",
    "    y=df['humidity'],\n",
    "    color='cornflowerblue',\n",
    "  )\n",
    "  ax2 = ax.twinx()\n",
    "  sns.lineplot(\n",
    "    ax=ax2,\n",
    "    x=time,\n",
    "    y=df['pressure'],\n",
    "    color='forestgreen',\n",
    "  )\n",
    "  sns.despine(\n",
    "    ax=ax, \n",
    "    top=True, \n",
    "    right=True, \n",
    "    left=False, \n",
    "    bottom=False, \n",
    "    offset=10\n",
    "  )\n",
    "  sns.despine(\n",
    "    ax=ax1, \n",
    "    top=True, \n",
    "    right=False, \n",
    "    left=False, \n",
    "    bottom=True, \n",
    "    offset=10\n",
    "  )\n",
    "  \n",
    "  sns.despine(\n",
    "    ax=ax2, \n",
    "    top=True, \n",
    "    right=False, \n",
    "    left=True, \n",
    "    bottom=True, \n",
    "    offset=80\n",
    "  )\n",
    "\n",
    "  ax.set_ylabel('temperature', color='orange')\n",
    "  ax1.set_ylabel('humidity', color='cornflowerblue')\n",
    "  ax2.set_ylabel('pressure', color='forestgreen')  "
   ]
  }
 ],
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